no code implementations • CVPR 2022 • Nikolay Patakin, Mikhail Romanov, Anna Vorontsova, Mikhail Artemyev, Anton Konushin
On the contrary, we propose GP$^{2}$, General-Purpose and Geometry-Preserving training scheme, and show that conventional SVDE models can learn correct shifts themselves without any post-processing, benefiting from using stereo data even in the geometry-preserving setting.
1 code implementation • CVPR 2023 • Dmitry Senushkin, Nikolay Patakin, Arseny Kuznetsov, Anton Konushin
In this work, we propose using a condition number of a linear system of gradients as a stability criterion of an MTL optimization.
1 code implementation • 18 May 2020 • Dmitry Senushkin, Mikhail Romanov, Ilia Belikov, Anton Konushin, Nikolay Patakin
Our second contribution is a novel training strategy that allows us to train on a semi-dense sensor data when the ground truth depth map is not available.
Ranked #1 on Depth Completion on Matterport3D